Integration of Machine Learning and Artificial Intelligence Techniques for Qualitative Research

This article proposes three methods of integrating machine learning (ML) and artificial intelligence (AI) techniques into qualitative data analysis procedure. Data science have revolutionized sectors like medicine, business, and psychology. This integration has led to the development of complex models, improving research understanding. While quantitative research has embraced ML and AI, their application in qualitative research remains underexplored. However, these techniques offer faster coding capabilities for thematic analysis compared to human analysis, though challenges arise in resolving conflicting codes. Despite this, ML and AI have the potential to enhance the depth of findings and offer triangulation in text data analysis. They should be viewed as tools to assist qualitative researchers rather than replacements for human analysis. Various integration approaches, such as natural language processing and artificial neural networks, can be employed at different stages of qualitative research, ultimately improving trustworthiness and relevance, especially in time-sensitive scenarios like public health emergencies

• Reflexive human–AI collaboration

  Open Access•Jovito Anito•Social Sciences & Humanities Open•2026

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• Applying machine-learning to rapidly analyze large qualitative text datasets to inform the Covid-19 pandemic response

  Open Access•Lauren B Towler, Lauren Towler et al.•Frontiers in Public Health•2023

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